Deep Learning With TensorFlow- A Complete Guide

December 13th, 2023
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Deep Learning With TensorFlow

If you are aware of machine learning, then you must also have heard about Deep Learning. To completely understand Deep Learning, you must also learn about what it entails and its libraries. In the long list, the one name that stands out is TensorFlow. While a Deep Learning with TensorFlow training will better be able to provide you with the right knowledge, here is a blog to help you get an idea about what it is. Let us understand these two and how you can benefit by getting certified in this field.

What is Deep Learning?

A subset of machine learning (ML), Deep Learning (DL) incorporates building and training of neural networks for solving complex problems. Deep Learning, today, is considered to be a powerful technique to accomplish multiple applications such as speech recognition, natural language processing, and computer vision.

There are various high-level libraries such as TensorFlow and Keras, which has led to Python becoming a popular programming language for DL.

If we are to talk about TensorFlow with Deep Learning, it has a layered architecture that supports an end-to-end approach. It majorly consists of three layers namely the output layer, hidden layers, and an input layer. A huge amount of data is needed to ensure the result given by DL algorithms is precise. Let's see what artificial neural networks are to understand DL in a better light.

Artificial Neural Networks

Artificial neural networks or ANNs function in a manner very similar to our biological neural networks. Herein, a node is the same as a neuron, links are the same as the axon, and the accepter algorithms of a perceptron are the same as the dendrites. As mentioned above, it is divided into three layers -

Artificial Neural Networks

1. Input Layer

The various neurons present in this layer consume data from eligible external environments. The input layer neither interacts with the data nor performs any computation.

2. Hidden Layers

This is where all the processing happens. Often also called the deep neural network, it extracts features, makes decisions, extracts valuable information from the data, and even predicts future actions.

3. Output Layer

Once the processing completes, all the data is transferred from the hidden layers to the output layer where computing is done. The processed information is then provided to the outside environment.

Why is TensorFlow So Popular?

Why is TensorFlow so popular? What is the reason behind this library being used so extensively?

Here are a few points that have led to TensorFlow being such a popular library.

  1. Widely used by Researchers- TensorFlow is a loved library by students as well as researchers everywhere for conducting research and model building.
  2. Used by Companies- A plethora of leading companies such as Google, Uber, AirBnb, Intel, and Dropbox are using TensorFlow extensively.
  3. Renders Machine Learning Easy- TensorFlow facilitates pre-trained data, high-level APIs, and models, all of which ensure that building machine learning models becomes an easy process.
  4. Facilitates using ML as a Service- ML can be used as a service with TensorFlow.
  5. Supports Pre-Made Models- Pre-made and pre-trained models are rendered by TensorFlow, making them instantly usable for experiments and production.

You may also like- Deep Learning Tutorial for Beginners

TensorFlow Features

For a library to be as famous as TensorFlow is, it has to have various features. Here is a list of some of the top TensorFlow features that you must know about-

  • Responsive construct
  • Easily trainable
  • Large and supportive community
  • Feature columns
  • Layered components
  • Parallel neural network training
  • Highly flexible
  • Open-source
  • Availability of statistical distributions

Deep Learning with TensorFlow

TensorFlow makes the use of multi-layered neural networks for accurately building complex applications. It is also being used for decision making, detecting anomalies, real-time object detection, audio manipulation, image processing, and video analysis.

Furthermore, TensorFlow offers structure and algorithms for implementing ML via decision trees and ANN to further compute humongous numerical datasets accurately.

Since we have already talked about the features of TensorFlow, it becomes clear why professionals prefer Deep Learning with TensorFlow. Let us look at the process of setting up TensorFlow for Deep Learning, after which we will move to a comparison of Keras vs TensorFlow vs PyTorch.

Setting Up TensorFlow for Deep Learning

Once you become a professional, you will need to ensure that the system can deliver essential computing power for performing DL algorithms on a dataset. This entails having the following –

  • Minimum Intel Core i3 Processor
  • 8 GB RAM
  • Ubuntu OS or Windows 10
  • NVIDIA geForce GTX 960

The steps for installation are:

  1. Install Anaconda from  https://www.anaconda.com/
  2. Install CUDA Toolkit from  Download CUDA toolkit
  3. Install cuDNN from NVIDIA cuDNN
  4. Create an Anaconda environment
  5. Install TensorFlow 

Keras vs TensorFlow vs PyTorch

Basis Keras TensorFlowPyTorch
Language Python Python, C++ & CUDALua 
Architecture Readable & simpleEasily understandable Complex 
Datasets Comparatively smaller Large datasets with high performanceLarge datasets
Speed Slow speed offering low performanceFast speed offering high performanceFast speed offering high performance 

Conclusion

The field of Deep Learning will continue to evolve. And this is where your opportunity to shine arises. Once you step into the field of deep learning, you will have the chance to continuously learn, grow, and advance your career prospects. This endless adventure is for those who are open to always learning and becoming better at what they do.

With Deep learning with TensorFlow training and certification, you will get all the best chances at succeeding. The advancements in TensorFlow are plenty and that also means you will have a great learning and growing graph for your future.

Deep Learning With TensorFlow FAQs (Frequently Asked Questions)

Q 1. Can you use TensorFlow for Deep Learning?

Ans. Google has developed TensorFlow primarily for Deep Learning applications. While TensorFlow also supports traditional ML, it is majorly used for deep learning.

Q 2. What TensorFlow is used for?

Ans. TensorFlow is widely used for developing models for different tasks such as handwriting recognition, image recognition, natural language processing, and various computational-oriented simulations.

Q 3. What is better PyTorch or TensorFlow?

Ans. Both TensorFlow and PyTorch are deep learning libraries. However, these two are different in many ways. TensorFlow provides better visualization than PyTorch. TensorFlow also stands better at deploying pre-trained models to production.

Q 4. Why is TensorFlow difficult?

Ans. TensorFlow is said to be difficult to both learn as well as use, mostly because of the high amount of programming skills involved.

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